Automated Planning: Theory and Practice

By Stefan Edelkamp. 2007

1,939 citations recorded by the source corpus.

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Citations in the corpus

Visual Semantic Planning Using Deep Successor Representations (2017, background): “The problem of generating a sequence of actions that leads to the goal state has been formally studied in the field of automated planning [14].”
Understanding the planning of LLM agents: A survey (2024, background): “Planning, as one of the most critical capabilities for agents, requires complicated understanding, reasoning, and decision-making progress [ Ghallab et al. , 2004 ] .”
Agent-based systems for manufacturing (2006, background): “Artificial intelligence (AI) provided a host of planning methods to solve this problem under various assumptions; many of them already have been proved to be applicable in real-life domains [44].”
Using General-Purpose Planning for Action Selection in Human-Robot Interaction (2016, background): “The general planning problem is usually divided into two parts: a description of the planning domain and a deﬁnition of a planning problem instance to be achieved within that domain (Ghallab, Nau, and Traverso 2004).”
Integrated task and motion planning in belief space (2013, methodology): “To support our hierarchical planning strategy we, instead, plan backwards from the goal using the process known as goal regression (Ghallab et al., 2004) in task planning and pre-image backchaining in motion planning (Lozano-Pérez et al., 1984).”
Learning High-Level Planning from Text (2012, background): “As a consequence, in many practical domains, planning algorithms rely on manually-crafted high-level abstractions to make search tractable (Ghallab et al., 2004; Lekav´y and N´avrat, 2007).”
On Learning Sparse Boolean Formulae for Explaining AI Decisions (2017, background): “Even though the decisions of intelligent agents are the consequence of algorithmic processing of perceived system and environment states [24,30], the straight-forward approach of reviewing this processing is not practical.”
Learning spatial relationships between objects (2011, background): “As a result, first-order logic is a language of choice for many planning problem descriptions and algorithms (Ghallab et al. 2004), as the goal of planning is the discovery of plans of action which can cause certain relationships between various objects in the domain of the problem to become true, r”
Classical Planning with Simulators: Results on the Atari Video Games (2015, background): “While all these games feature a known initial (RAM) state and actions that have deterministic effects, the problem of selecting the next action to be done cannot be addressed with off-the-shelf classical planners [Ghallab et al., 2004; Geffner and Bonet, 2013].”
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning (2023, background): “However, when a faithful simulator is not available or is expensive to use, collecting feedback through actual plan execution can be costly and may not fully exploit the advantages of provably sound planning, as seen in classical-planning literature [11, 13].”
Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology) (2007, background): “Means-ends reasoning is perhaps better known in the AI community as planning [49].”